INTRODUCTION

Plotly is a versatile and powerful data visualization library that finds extensive use in the R programming language. It provides an interactive and dynamic environment for creating a wide range of visualizations, including plots, charts, and dashboards. Plotly supports various chart types, such as scatter plots, line charts, bar charts, and more.

One of the key features of Plotly is its interactivity, allowing users to zoom, pan, and hover over data points for detailed insights. Additionally, it facilitates the creation of interactive dashboards, making it a valuable tool for data exploration and presentation.

In R, Plotly can be seamlessly integrated with other data science and statistical libraries, enhancing its utility in exploratory data analysis and model interpretation. With its user-friendly syntax and interactive capabilities, Plotly has become a popular choice for professionals like yourself who engage in data science and visualization within the realm of physics and related fields.

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Why use plotly?

• Dynamic Visualization: plotly enables dynamic and interactive visualization in machine learning and AI contexts.

• Exploring Complex Patterns: Data scientists use plotly to visually explore intricate patterns and relationships within datasets.

• Model Evaluation : In machine learning model development, plotly aids in visualizing and comparing training and validation metrics.

• Interpreting Prediction : plotly supports the interpretation of model predictions through interactive charts, enhancing transparency in AI systems.

• Seamless Integration : The easy integration of plotly into machine learning workflows enhances overall interpreting ability and communication of results.

• Enhanced Communication : plotly’s visualizations contribute to a visually compelling representation of AI and machine learning findings, facilitating effective communication.

VISUALIZATIONS

Here, my implementation method will first plot using ggplot2 and then make the plot interactive using ggplotly. Here, I have small code chunk. The code chunk makes sure that to import the libraries, if not installed, it installs and import the libraries.

if (!require(ggplot2)){
  install.packages("ggplot2")
  library(ggplot2)
}

if (!require(ggdark)){
  install.packages("ggdark")
  library(ggdark)
}

if (!require(plotly)){
  install.packages("plotly")
  library(plotly)
}

I have programmed a complicated looking code chunk which gives me visualization of normal distribution. There are more efficient way to make it. But I just wanted the plot to work and it works.

# Set seed for reproducibility
set.seed(1234)

# Generate random data following a normal distribution
data <- rnorm(10000, mean = 0, sd = 1)

# Calculate mean and standard deviation
mu <- mean(data)
sigma <- sd(data)

# Create a data frame
df <- data.frame(x = data)

# Define custom x-axis labels as character strings
custom_labels <- c("μ - 3σ", "μ - 2σ", "μ - σ", "μ", "μ + σ", "μ + 2σ", "μ + 3σ")

# Function to calculate normal density
normal_density <- function(x) dnorm(x, mean = mu, sd = sigma)

# Calculate the area under the curve between mu - sigma and mu + sigma
area_sigma <- integrate(normal_density, mu - sigma, mu + sigma)$value
area_2sigma <- integrate(normal_density, mu - 2 * sigma, mu + 2 * sigma)$value
area_3sigma <- integrate(normal_density, mu - 3 * sigma, mu + 3 * sigma)$value

# Plot the normal distribution curve with custom x-axis labels, vertical lines, and shaded areas
myplot <- ggplot(df, aes(x = x)) +
  stat_function(fun = normal_density, color = "blue", size = 1) +
  geom_vline(xintercept = c(mu - sigma, mu + sigma), linetype = "dashed", color = "red") +
  geom_vline(xintercept = c(mu - 2 * sigma, mu + 2 * sigma), linetype = "dashed", color = "purple")+
  geom_vline(xintercept = c(mu - 3 * sigma, mu + 3 * sigma), linetype = "dashed", color = "black")+
  geom_ribbon(aes(ymax = normal_density(x), ymin = 0), fill = "yellow", alpha = 0.5) +
  ggtitle("Normal Distribution Curve") +
  xlab("Number of σ from mean") +
  ylab("Probability Density") +
  scale_x_continuous(breaks = c(mu - 3 * sigma, mu - 2 * sigma, mu - sigma, mu, mu + sigma, mu + 2 * sigma, mu + 3 * sigma),
                     labels = custom_labels) +
  annotate("text", x = mu, y = c(0.1, 0.5, 1.0), 
           label = c(sprintf("Area(σ) = %.4f", area_sigma),
                     sprintf("Area(2σ) = %.4f", area_2sigma),
                     sprintf("Area(3σ) = %.4f", area_3sigma)),
           vjust = 0.5, hjust = 0.5, size = 4, color = "black") 

myplot

Now that my plot works, let me use ggdark to make it dark themed. This not necessary but I find dark themes cool and it makes me look more serious and geeky/nerd.

While was trying different dark themes available in the ggdark library, the color of text annotation and background, both were black. Due to this, the text annotation was invisible. Once, changed the theme, as the text was black, I changed the color of text annotation to white.

After solving this problem, I thought that I can directly use my myplot variable in ggplotly function to get an interactive plotly plot in a different code chunk. I did implement that and the generated the interactive plot. But there was some kind of error while I was trying to knit my .Rmd file. After sometime, I just implemented this in the below code chunk. Instead of make two separate code chunks, the knit worked with two code chunks.

myplot <- myplot + ggdark::dark_theme_gray() + annotate("text", x = mu, y = c(0.1, 0.5, 1.0), 
           label = c(sprintf("Area(\u03C3) = %.4f", area_sigma),
                     sprintf("Area(2\u03C3) = %.4f", area_2sigma),
                     sprintf("Area(3\u03C3) = %.4f", area_3sigma)),
           vjust = 0.5, hjust = 0.5, size = 4, color = "white")

ggplot_myplot <-  ggplotly(myplot)
ggplot_myplot

Although, I have generated the interactive plot and almost happy of what I got. It took more time than I expected. Mainly, because of the knit. Finally, my thoughts on this project is even though ggplot2 is good, it is always better to know other alternatives to it.